Codex Skill for Auto-Organizing Plug-and-Play Modules: A Practical Guide to Boosting Research Efficiency

The Pain of Repetitive Work in Research

In deep learning research, "Plug-and-Play Modules" are an incredibly practical technique — find an effective module from a paper, integrate its code into your own model, and quickly validate the results. The core idea behind plug-and-play modules is to encapsulate a specific function (such as attention mechanisms, feature enhancement, multi-scale fusion, etc.) into an independent network sub-module that can be directly inserted at specific positions in an existing model without modifying the overall backbone architecture. Typical plug-and-play modules include the channel attention module from SE-Net, the mixed attention module from CBAM, and various spatial pyramid pooling structures. These modules typically maintain consistent input-output dimensions, allowing them to be flexibly combined like LEGO bricks, significantly lowering the barrier for technical validation.

However, the process of organizing these modules is quite tedious: you need to read papers, locate key figures, understand module structures, extract code, verify runnability… Each module can easily take several hours to organize.

A Bilibili content creator shared a clever solution: using OpenAI Codex to build a dedicated Skill that fully automates the organization of plug-and-play modules and writes the results directly into Feishu (Lark) documents, dramatically boosting research productivity.

Core Workflow of the Codex Skill

Just Three Parameters to Start Auto-Organization

OpenAI Codex is a code generation and comprehension system from OpenAI. It can not only generate code from natural language descriptions but also read and understand code repositories, analyze paper content, execute code verification, and more. The "Skill" concept in Codex is similar to a predefined automation task template — users can trigger a series of complex automated operations by configuring parameters. Unlike simple ChatGPT conversations, Skills emphasize repeatable execution, structured output, and toolchain integration, making them closer to scripts or pipelines in traditional software engineering.

This Codex Skill is remarkably simple to use. Users only need to provide three pieces of information:

1. Paper name: Specify the target paper to analyze
2. Module name: Specify the particular plug-and-play module to organize
3. Feishu document link: The target document where results will be written

Once invoked, Codex automatically completes all analysis and organization work, writing the results into the specified Feishu document. Choosing Feishu documents as the output destination — rather than local files or other formats — reflects the modern trend toward research collaboration and knowledge management. Feishu documents support rich text, code blocks, multi-person collaborative editing, and version history, making them ideal for research notes and technical documentation. By writing Codex output directly to Feishu via API, researchers can build a continuously growing plug-and-play module knowledge base that's easy to share across teams and retrieve later. This "AI generation + cloud document accumulation" pattern is becoming the standard workflow for an increasing number of research teams. The entire process requires no manual intervention — just wait a moment and you'll have a complete module document.

Multi-Skill Collaboration: A Modular Design Approach

You might not have noticed, but this Skill wasn't built from scratch. Instead, it adopts a nested Skill invocation architecture. The author had previously created a Skill called "Paper Info" for extracting basic paper information. The new module organization Skill first calls Paper Info to get an overview of the paper, then performs deeper analysis on top of that.

Nested Skill invocation is a design pattern that decomposes complex tasks into multiple reusable subtasks, similar to function calls or microservice architecture in software engineering. In this case, the "Paper Info" Skill handles extracting the paper's basic metadata (such as title, authors, abstract, code repository URL, etc.), while the module organization Skill performs deeper analysis on that foundation. The advantages of this layered design are: each Skill can be independently tested and optimized, new Skills can freely combine existing Skill capabilities, redundant development is avoided, and the entire system becomes easier to maintain and extend.

This modular Skill design philosophy itself embodies the principle of "reuse" in software engineering — perfectly aligned with the philosophy of plug-and-play modules.

Detailed Breakdown of Auto-Generated Documents

Precise Location of Paper Figures

Since there are currently no good tools for automatically cropping paper figures, the Codex Skill takes a compromise approach: it uses text to indicate which Figure number in the paper corresponds to the module, and which specific part of that figure the module corresponds to.

Although users still need to manually look up the corresponding paper figures, this approach already dramatically reduces the cost of locating them. For example, in the demo, the Skill accurately identified that the module is shown in Figure 2 of the paper — the user simply needs to open the paper and find the corresponding figure.

Module Explanation and Code Implementation

The documents generated by the Codex Skill contain the following key content:

• Module description location in the paper: Indicates which Section contains the detailed description
• Detailed module explanation: A complete textual explanation of the module's structure and principles
• Complete code implementation: Module code extracted from the source, with detailed comments
• Source code location: Annotates the specific file path and location in the original repository

Code Verification and Data Flow Description

The Skill also automatically simulates input data to verify whether the extracted code runs correctly. This step is known in engineering as a "Smoke Test." For plug-and-play modules, the core verification confirms whether the module's input-output tensor dimensions are correct, whether there are missing dependencies, and whether forward propagation completes normally. Codex typically constructs a random tensor matching the module's expected input shape (e.g., a feature map with batch_size=1), executes one forward pass, and checks whether the output shape matches expectations. This automatic verification mechanism helps researchers rule out obvious code errors before integration, avoiding wasted time on debugging environment configurations.

Finally, a data flow description is appended to help users understand how data flows through the module. This step is critical for integrating plug-and-play modules into your own model — researchers need to clearly understand what input tensor shape the module expects, what transformation steps it goes through, and what kind of feature representation it ultimately outputs, in order to correctly embed the module at the appropriate position in their network.

Practical Usage Tips and Research Workflow

The author also acknowledges that while the Codex Skill can quickly organize all the information about a module, using it effectively still requires following a certain research workflow:

1. Understand the background: First, look at the context in which the module was proposed and what problem it solves
2. Quick validation: If it seems applicable, directly integrate the code into your model and run it
3. Analyze results: If the results are good, go back and deeply analyze why the module works
4. Tell the story: Finally, articulate the relevant technical narrative in your paper

This "run it first, understand it later" strategy, combined with the Codex Skill's auto-organization capability, allows researchers to screen a large number of candidate modules in a short time, dramatically accelerating experimental iteration. This methodology is particularly well-suited for deep learning experiments, because the actual effectiveness of many modules often diverges from theoretical expectations — rather than spending extensive time analyzing each module's theoretical advantages on paper, it's better to experiment quickly, let the data speak, and then deeply understand and theoretically explain the approaches that actually work.

Conclusion: AI Engineering Empowers Research Efficiency

This case demonstrates the enormous potential of OpenAI Codex as a research assistance tool. Through carefully designed Skills, the work of organizing plug-and-play modules — which previously took hours — is compressed into an automated process completed in minutes. More importantly, it embodies an approach of engineering AI capabilities — not simply engaging in conversational Q&A, but building reusable, composable automated workflows. This approach is closely aligned with the recently emerging concept of "AI Agents": combining the capabilities of large language models with external tools (such as API calls, code execution, and document writing) to form intelligent systems capable of autonomously completing complex multi-step tasks.

For deep learning researchers, the value of such tools lies not in replacing thinking, but in eliminating repetitive labor, allowing researchers to focus their energy where creativity is truly needed. If you also find yourself frequently organizing module code from papers, consider using Codex Skills to build your own automation pipeline.